DOE's ARPA-E funds 6 projects to improve sorghum development

The U.S. Department of Energy’s Advanced Research Projects Agency has awarded $30 million to six projects under its Transportation Energy Resources from Renewable Agriculture program. TERRA projects aim to accelerate energy crop development for the production of renewable transportation fuels from biomass.

According to ARPA-E, the TERRA program integrates agriculture, information technology and engineering to address major global challenges in developing crops that are sustainable, affordable and yield abundant plant feedstocks for bioenergy. The program will encourage systems that couple large-scale physical and genetic characterization with advanced algorithms in order to accelerate the year-over-year yield gains of traditional plant breeding and the discovery of crop traits that improve water productivity, nutrient use and our ability to mitigate greenhouse gases. The six TERRA awards support the development of improved varieties of sorghum.

The six project teams are expected to address the limitations surrounding crop phenotyping, which is identification and measuring of the physical characteristics of the plants, and genotyping, which is decoding of plant DNA. Project teams will develop mobile platforms with sensory systems to observe and record the characteristics of plants and create advanced algorithms to analyze data and predict plant growth potential. According to ARPA-E, the TERRA program will also fund the creation of a large public database comprised of sorghum genotypes and field phenotypes. This database will provide the greater community of plant physiologists, bioinformaticians and geneticists with the knowledge to improve sorghum and bioenergy crops.

The six TERRA projects include:

Clemson University: Breeding High Yielding Bioenergy Sorghum for the New Bioenergy Belt- $6 million Clemson, South Carolina-based Clemson University, along with Carnegie Mellon Robotics Institute and partners, while phenotype international germplasm and plant varieties. Researchers will design and build phenotyping platforms that can collect visual imagery, hyperspectral imagery and 3D shape data of test crops multiple times daily. The ground and aerial platforms will have the ability to directly contact the plant, and the team will use cameras and imaging algorithms to develop 3D models of individual plants and their canopy structure, impellent machine-learning techniques to analyze the data gathered, and translate that into predictive algorithms for breeding and improved biofuel sorghum hybrids.

St. Louis-based Donald Danforth Plant Science Center, along with its research partners, will coordinate a national network of test sites in Arizona, Kansas, Missouri, South Carolina and Texas to provide broad environmental and genetic diversity essential for understanding phenotype behavior. The team will host a plant phenotyping system that provides high-resolution evaluation of crops grown under field conditions. In addition, comprehensive genomic analyses will be conducted to create a high-quality reference dataset of energy sorghum’s physical characteristics and genetic information. The project will provide data in community-defined formats that will be made available to researchers in a high-performance computing environment and archived for public use.

Pacific Northwest National Laboratory: Consortium for Advanced Sorghum Phenomics - $3.3 millionRichland, Washington-based PNNL and its research partners will utilize novel phenotyping platforms, predictive modeling techniques and image processing tools to generate maps of plant composition and predict plant growth. The project will focus on simulating drought and salinity stresses in order to develop plant varieties that are more resilient to these environmental challenges. PNNL will perform molecular phenotyping to identify breeding markers for these biotic stresses. In addition, Blue River Technologies will develop autonomous phenotyping systems that can create 3D models of individual plants and construct pointcloud data sets used to produce the plant composition maps, while Chromatin Inc. will advance improved commercial seed cultivars.

Purdue University: Automated Sorghum Phenotyping and Trait Development Platform - $6.5 million West Lafayette, Indiana-based Purdue University, along with IBM Research and partners, will acquire and utilize data to develop predictive models for plant growth and to design and implement methods for identifying genes controlling sorghum performance. The team will create a system that combines data streams from groundbased and mobile platforms for automated phenotyping. Advanced image and signal processing methods will extract phenotypic information to produce predictive models for plant growth and development. The team will also use high-performance computing platforms and prediction algorithms to analyze and identify links between plant characteristics and their underlying genetics. The end goal is to develop a user-friendly system that will enable breeders and other end users to interact with the data and analytics.

Texas A&M AgriLife Research: Automated Phenotyping System for Genetic Improvement of Energy Crops - $3.1 millionCollege Station, Texas-based TAMU, along with the National Robotics Engineering Center and Partners, will develop an phenotyping system consisting of a suite of sensors mounted on a durable, groundbased, field deployable, mobile robotics platform. The system will employ an extendable, mechanical arm that can penetrate the dense plant canopy to capture images and measurements from above, within and below the crop, yielding previously unattainable sensor data. The team will use TAMU’s existing collection of sorghum varieties and will employ machine vision and learning algorithms to process the data for predictive modeling of plant growth.

University of Illinois at Urbana-Champaign: Mobile Energy-Crop Phenotyping Platform - $3.1 millionChampaign, Illinois-based UIUC, with its partners Cornell University and Signetron Inc., will develop small-scale, automated ground rovers with the capability to travel within the crops between rows. Phenotyping platforms will measure crop growth via 3D reconstruction of plants and stands and assess physiological indicators of performance using reflectance and LiDAR (laser light detection and ranging) sensors. The team will also use biophysical growth models and DNA-sequencing technologies to develop methods for accelerating improvement of energy sorghum and identifying key genes that control plant performance.